Because DNNs are black-box models, they lack explanatory power behind their predictions. This is a critical issue, as the scientific method requires a good understanding of the system and the causes of the results. Neural additive models (NAMs) have recently been proposed to help explain how DNNs reach their prediction values [1]}. NAMs assign targets to feature-net base modules that represent each variable. Then, based on their changes, the reasons for the DNN output are better understood.
